Motion blur estimation and restoration using light trails

ABSTRACT

A method for processing a blurred image ( 16 ) that includes a captured light trail ( 24 ) includes the steps of: (i) displaying the blurred image ( 16 ) on an image display ( 36 ); (ii) manually identifying an image patch ( 25 ) that includes the captured light trail ( 24 ) in the displayed blurred image ( 16 ); (iii) calculating a point spread function for the captured light trail ( 24 ) with a control system ( 28 ); and (iv) deblurring at least a portion of the blurred image ( 16 ) with the control system ( 28 ) utilizing the calculated point spread function.

BACKGROUND

Cameras are commonly used to capture an image of a scene that includesone or more objects. Unfortunately, some of the images are blurred. Forexample, movement of the camera and/or movement of the objects in thescene during the exposure time of the camera can cause the image to beblurred. Further, if the camera is not properly focused when the imageis captured, that image will be blurred.

SUMMARY

The present invention is directed to a method for processing, e.g.deblurring and restoring a blurred image that includes a light trail. Inone embodiment, the method includes the steps of (i) displaying theblurred image on an image display; (ii) manually identifying an imagepatch in the displayed blurred image; and (iii) calculating a pointspread function for at least a portion of the image patch with a controlsystem. In certain embodiments, the manually identified image patchincludes a captured light trail. Further, the step of calculating apoint spread function includes calculating the point spread function forthe captured light trail. With this design, in certain embodiments, thepresent invention is directed to a semi-automatic method to addressmotion blur estimation and restoration for general image types (nightscene and others).

In one embodiment, the step of calculating a point spread functionincludes the steps of (i) identifying pixels in the image patch thathave captured part of the captured light trail as light trail pixels,and (ii) identifying pixels in the image patch that have not capturedpart of the captured light trail as background pixels. Further, thepoint spread function can be calculated for the identified light trailpixels.

As provided herein, in certain embodiments, a portion or the entireblurred image can be deblurred and restored utilizing the calculatedpoint spread function. Further, in certain embodiments, the point spreadfunction can be calculated utilizing a blind or non-blind deconvolutionmethod. In yet another embodiment, the method includes the steps of:displaying the blurred image on an image display; manually identifyingan image patch that includes the captured light trail in the displayedblurred image; calculating a point spread function for the capturedlight trail with a control system; and deblurring at least a portion ofthe blurred image with the control system utilizing the calculated pointspread function.

The present invention is also directed to a computer including an imagedisplay that displays the image, an identifier that can be used tomanually identify the light trail, and a control system that utilizesthe method disclosed herein to restore the image.

In still another embodiment, the present invention is directed to acamera including (i) a capturing system for capturing the captured imageof the scene; (ii) an image display for displaying the captured image ofthe scene; (iii) an identifier for manually identifying an image patchin the displayed captured image in the event the displayed capturedimage includes a light trail; and (iv) a control system that calculatesa point spread function for at least a portion of the image patch.

Further, the control system can (i) identify light trail pixels in theimage patch that have captured part of the captured light trail, (ii)identify background pixels in the image patch that have not capturedpart of the captured light trail, and (iii) calculate the point spreadfunction for the identified light trail pixels. Moreover, the controlsystem can deblur at least a portion of the blurred image utilizing thecalculated point spread function.

BRIEF DESCRIPTION OF THE DRAWINGS

The novel features of this invention, as well as the invention itself,both as to its structure and its operation, will be best understood fromthe accompanying drawings, taken in conjunction with the accompanyingdescription, in which similar reference characters refer to similarparts, and in which:

FIG. 1 is a simplified view of a scene, an image apparatus havingfeatures of the present invention, a raw, first captured image of thescene, a raw, second captured image of the scene, and an adjusted image;

FIG. 2 is a simplified illustration of a computer system having featuresof the present invention;

FIG. 3 is a flow chart that illustrates a method having features of thepresent invention; and

FIG. 4 is a simplified illustration of an image patch from the blurredimage.

DESCRIPTION

FIG. 1 is a simplified perspective view of an image apparatus 10 (e.g. adigital camera) having features of the present invention, and a scene12. FIG. 1 also illustrates a raw first captured image 14 (illustratedaway from the image apparatus 10), and a raw second captured image 16(illustrated away from the image apparatus 10), each captured by theimage apparatus 10. In FIG. 1, the first captured image 14 is intendedto illustrate a sharp image 18 (with sharp edges and straight lines) andthe second captured image 16 is intended to illustrate a blurred image20 (with weak edges and wavy lines). For example, movement of the imageapparatus 10 during the capturing of the blurred image 16 can causemotion blur 20 in the image 14.

As an overview, in one embodiment, the present invention is directed toa semi-automatic method to address motion blur estimation andrestoration for general image types (night scene and others). Forexample, the present method can be used for general image types with thepresence of motion light trails 24. However, the present inventionrelies on the user to identify an image patch 25 (sometimes referred toas a “selected area”) that contains one or more motion blur light trails24. Subsequently, in certain embodiments, the blurred image 16 can beautomatically post-processed to reduce the amount of image blur 20 tocreate an adjusted image 26.

In one embodiment, the image apparatus 10 includes a built in controlsystem 28 (illustrated in phantom) that uses the unique method disclosedherein for identifying the light trail 24, and restoring the blurredimage 16 to create the adjusted image 26 with a reduced level of blur20.

Alternatively, referring to FIG. 2, the blurred image 16 can betransferred to a computer 248 having a control system 228 that uses theunique method for identifying and restoring the image 16 that includes alight trail 24 to create the adjusted image 26 (illustrated in FIG. 1).Still alternatively, the blurred image 16 can be transferred to awebsite (not shown) having a control system that uses the unique methodfor identifying and restoring the image 16 that includes a light trail24 to create the adjusted image 26.

With the present invention, in certain embodiments, the user manuallyidentifies one or more of the light trails 24 in the blurred image 16.Next, the control system 28, 228 determines a point spread function forthe identified light trail 24. Subsequently, the control system 28, 228deblurs and restores the blurred image 16 using the determined pointspread function. Because, with the present invention, the light trail 24is manually identified by the user, less computational power is requiredas opposed to an automatic identification of the light trail 24 with thecontrol system 28, 228. This can allow for in camera correction of ablurred image 16.

Further, because the shape, direction, and other characteristics oflight trails 24 are very random, the number of incorrectly identified(false positives) light trails 24 is reduced utilizing manualidentification of the light trail 24. In contrast, in order to reducethe number of false positives in a completely automated method, variousconstraints can be imposed on the automatic identification of the lighttrails. However, these constraints can degrade the ability to detectmotion blur images. Stated in another fashion, it is extremelychallenging for an automatic algorithm to correctly identify motionrelated light trails for general images without introducing certainamount of false positives.

Referring back to FIG. 1, the type of scene 12 captured by the imageapparatus 10 can vary. For example, the scene 12 can include one or moreobjects 30, e.g. animals, plants, mammals, light sources, and/orenvironments. For simplicity, in FIG. 1, the scene 12 is a night scenethat is illustrated as including a simplified stick figure of a person30A, and two spaced apart light sources 30B (each illustrated as acircle). In contrast, the scene may not include a person or any lightsources, or the blurred image 16 with the light trail 24 may not be anight scene. Further, as non-exclusive examples, the light source 30B inthe scene 12 can be the result of a light producing element, areflection, or another type light producing event.

In FIG. 1, the image apparatus 10 is a digital camera, and includes anapparatus frame 32, an optical assembly (not shown), and a capturingsystem 33 (illustrated as a box in phantom), in addition to the controlsystem 28. The design of these components can be varied to suit thedesign requirements and type of image apparatus 10. Further, the imageapparatus 10 could be designed without one or more of these components.Additionally or alternatively, the image apparatus 10 can be designed tocapture a video of the scene 12.

The apparatus frame 32 can be rigid and support at least some of theother components of the image apparatus 10. In one embodiment, theapparatus frame 32 includes a generally rectangular shaped hollow bodythat forms a cavity that receives and retains at least some of the othercomponents of the camera. The apparatus frame 32 can include a shutterbutton 34 that causes the capturing system 33 to capture the image 14,16.

The optical assembly can include a single lens or a combination oflenses that work in conjunction with each other to focus light onto thecapturing system 33. In one embodiment, the image apparatus 10 includesan autofocus assembly (not shown) including one or more lens movers thatmove one or more lenses of the optical assembly in or out until thesharpest possible image of the subject is received by the capturingsystem 33.

The capturing system 33 captures information for the images 14, 16. Thedesign of the capturing system 33 can vary according to the type ofimage apparatus 10. For a digital-type camera, the capturing system 33includes an image sensor (not shown) e.g. charge coupled device, afilter assembly (not shown) e.g. a Bayer filter, and a storage system(not shown). The storage system can be fixedly or removable coupled tothe apparatus frame 32. Non-exclusive examples of suitable storagesystems include flash memory, a floppy disk, a hard disk, or a writeableCD or DVD.

The control system 28 is electrically connected to and controls theoperation of the electrical components of the image apparatus 10. Thecontrol system 28 can include one or more processors and circuits, andthe control system 28 can be programmed to perform one or more of thefunctions described herein. Further, in this embodiment, the controlsystem 28 is positioned within the apparatus frame 32 for in cameraprocessing of the images 14, 16. Alternatively, a control system thatutilizes the algorithm disclosed herein can be separate from the camera(e.g. a computer or a website) that performs post-processing on theimages 14, 16.

The image apparatus 10 can include a camera image display 36 thatdisplays control information, control switches, and/or other informationthat can be used to control the various functions of the image apparatus10. Further, the camera image display 36 can display low resolution thruimages captured by the image apparatus 10.

Additionally, the camera image display 36 can display the raw capturedimages 14, 16 and/or the adjusted image 26. With this design, a user candecide which images 14, 16 are sharp and do not need further processing,and/or which images 14, 16 are blurred and need post-processing. Forexample, upon review of the first captured image 14, the user candetermine that this image is sharp 18 and that no additional processingis necessary. Alternatively, upon review of the second captured image16, the user can determine that this image is blurred 20 and requiresfurther processing.

When a point of light 30B from a scene 12 is captured while the camera10 is held steady, the captured point of light 38 appears as a diskshaped light (illustrated as a circle in FIG. 1) when in focus in thesharp image 18. Alternatively, when the point of light 30B from thescene 12 is captured while the camera is being moved (e.g. shaked), theresulting image 16 can include a light trail 24 around the location ofthe light source 30B, where the light trail 24 reflects and correspondsto the motion of the camera 10 during the capturing of the image 16. Inthis example, the light trail 24 has a trace direction 40 (illustratedwith an arrow) that reflects and corresponds to the motion of the camera10 during capturing of the image. For example, the second captured image16 was captured while moving the camera 10 sideways (horizontally). As aresult thereof the light trails 24 in the second captured image 16extend generally horizontally. Usually, the camera 10 shake is not assimple as the horizontal or linear direction 40 like the exampleillustrated in FIG. 1. Typically, however, the camera 10 shake israndom. As a result thereof, the light trail 24 is random shaped.

It should be noted that, for example, the light trails 24 can be causedby camera motion while capturing light in the scene. Alternatively, thelight trails 24 can be caused by saturated light, near saturated light,high lights, reflections, or other sources inside the scene. As providedherein, one or more of the light trails 24 may not associate exactly toa real point source. For example, a night city scene may contain windowlights from skyscrapers. With this type of scene, if the camera 10 isshaken while the image is captured, this will result in light trailsrelated to these window lights.

As provided herein, in certain embodiments, with the present invention,the user can decide that the image 14 is sharp and does not include anylight trails 24. In this case, no other processing of the image 14 isnecessary. Alternatively, the user can identify the image 16 as beingblurred, and subsequently identify one or more of the light trails 24 inthe image 16. Next, the control system 28, 228 can reduce the amount ofblur 20 in the second image 16 to provide the adjusted image 26.

In one embodiment, the image apparatus 10 can include an identifier 42that can be used by the user (e.g. person operating the camera) tomanually identify an image patch 25 containing one or more of the lighttrails 24. For example, the identifier 42 can include one or morecontrol switches (e.g. buttons or dials) that are electrically connectedto the control system 28. Alternatively, as another non-exclusiveexample, the camera image display 36 can be a touch screen thatfunctions as the identifier 42. In any event, the identifier 42 can beany type of controller that is operated by the user that allows for themanual identification of the image patch 25.

Similarly, referring to FIG. 2, the computer 248 can include a computerimage display 236 that displays the raw images 14, 16. With this design,the user can decide which images 14, 16 are sharp and do not needfurther processing, and/or which images 14, 16 are blurred and needpost-processing.

Further, the computer 248 can include an identifier 242 that can be usedby the user to manually identify an image patch 25 including one or moreof the light trails 24. For example, the identifier 242 can be a mouseor a keyboard that is electrically connected to the control system 228that allows the user to manually identify the image patch 25.Alternatively, as another non-exclusive example, the computer imagedisplay 236 can be a touch screen that functions as the identifier 242.

The type of manual identification of the image patch 25 containing thelight trail 24 can vary. In one non-exclusive embodiment, the user canuse the identifier 42, 242 to create an outline 44 that defines theboundary of the image patch 25 and encompasses (e.g. encircles) one ormore light trails 24. As non-exclusive examples, the outline 44 can be arectangular shaped box, a circle, an oval or another type of shape.Alternatively, as other non-exclusive examples, the user can identifypoints around one or more light trails 24, or trace around the perimeterof one light trail 24 to identify the image patch 25.

In example illustrated in FIGS. 1 and 2, the user has created a box 44with the identifier 42, 242 that defines the image patch 25 thatincludes pixels that have captured a single light trail 24.

In certain embodiments, the present invention will work if two or moreseparate light trails 24 are included in the selected image patch 25.Still alternatively, in certain embodiments, the user can manuallyidentify multiple image patches 25, with each image patch 25 having atleast one light trail 24. In this embodiment, the control system 28, 228can process one or more of the selected image patches 25. If multipleimage patches 25 are processed, the information from the multiple imagepatches 25 can be combined in some fashion to determine the point spreadfunction for the captured image.

As another non-exclusive example, the image patch 25 can be identifiedby cropping the image 16.

FIG. 3 is a flow chart that illustrates one embodiment of onenon-exclusive method 300 having features of the present invention.First, at step 302, the blurred image is displayed on the image display.Subsequently, at step 304, the image patch including the light trail ismanually identified by the user in the blurry image. FIG. 4 is asimplified illustration of an enlarged image patch 425 (“selected area”)taken from the blurred image 16 (illustrated in FIG. 1) that wasmanually selected by the user. In this embodiment, the image patch 425includes a single light trail 424. The image patch 425 is defined by aplurality of pixels 450 (illustrated as dashed boxes). The shape of theimage patch 425 and the size of the image patch 425 (e.g. number ofpixels 450 that make up the image patch 425) can be varied pursuant tothe teachings provided herein. For example, in certain embodiments, theuser selects the shape and size of the image patch 425 via control ofthe identifier 42. Alternatively, the control system 28 can limit thesize and shape options that are available for selection by the user.

In one non-exclusive example, the image patch 424 includes betweenapproximately twenty (20) and one hundred (100) pixels. However, itshould be noted that the number of pixels in the image patch 424 can bevaried by a number of factions including the size of the light trail 424and the resolution of the original blurred image 16.

Referring back to FIG. 3, next, at step 306, the control system analyzesat least a portion of the image patch and generates a point spreadfunction for at least a portion of the image patch. In one non-exclusiveembodiment, the control system can analyze the image patch and determinewhich pixels have captured a portion of the light trail. Subsequently,the point spread function can be calculated utilizing the pixels thatmake up the light trail.

More specifically, referring to FIG. 4, as provided herein, the pixels450 in the image patch 425 can be categorized as either light trailpixels 452 or background pixels 454. Light trail pixels 452 are thepixels in the image patch 425 that have captured part of the capturedlight trail 424. In contrast, background pixels 454 are the pixels inthe image patch 425 that have not captured part of the captured lighttrail 424.

In this embodiment, the control system 28 (illustrated in FIG. 1) cananalyze the image patch 425 to (i) identify and label pixels 450 in theimage patch 425 that have captured part of the captured light trail 424as light trail pixels 452, and (ii) identify and label pixels 450 in theimage patch 425 that have not captured part of the captured light trail424 as background pixels 454. Next, the control system 28 can create abackground mask that masks out the background pixels 454.

As one non-exclusive example, a simple segmentation algorithm can beused to identify and separate the light trail pixels 450 from thebackground pixels 452 in the image patch 425. Many differentsegmentation techniques can be utilized. In one embodiment, the maximumcolor value for the color channels is identified, and subsequently, themaximum color value is compared to some thresholds for identifying thelight trail pixels 450. As one non-exclusive example, the segmentationtechnique can utilize two levels of thresholds, with the first level(“low threshold”) being relatively low, and the second level (“highthreshold”) being close to saturation point. As a non-exclusive example,the low threshold can be approximately 192 and the high threshold can beapproximately 240. However, other values can be used for the thresholds.

In this embodiment, the light trail pixels 450 will have a maximum colorvalue higher than the low threshold. In addition, the identified lighttrail regions will also contain a certain percentage of pixels with acolor value that is greater than the high threshold. As a non-exclusiveexample, the certain percentage can be ten percent or greater.

The remaining pixels in the image patch 425 that do not meet thiscriteria are classified as background pixels 452. With this procedure,the light trail pixels 450 and the background pixels 452 can beidentified.

Subsequently, in certain embodiments, the point spread function can becalculated for the light trail 424 using the identified light trailpixels 452.

It should be noted that in certain embodiments, the control system 28,228 can evaluate neighboring pixels that are nearby and outside theselected image patch 425 to insure that the entire light trail 424 isused to calculate the point spread function even if the user incorrectlycreated an image patch 425 that did not include the entire light trail424. With this design, in certain designs, certain neighboring pixelscan also be classified as light trail pixels 452.

Many alternative methods can be utilized to identify the point spreadfunction (“PSF”) kernel for the light trail pixels 452. For example, asimple method (e.g. a mapping method) can be utilized to estimate thePSF kernel, or a more complicated PSF estimation algorithm can beutilized. Still alternatively, the simple PSF method can be utilized togenerate a simple PSF kernel that is then refined with the morecomplicated PSF estimation algorithm. As provided herein, many existingPSF estimation algorithms do not handle saturated pixels very well.

In one embodiment, the PSF estimated by the simple PSF estimation methodcan be used as an initialization PSF for a more sophisticated PSFestimation algorithm. In this example, the more sophisticated PSFestimation algorithm can utilize image regions that do not containsaturated lights.

In one embodiment, the image patch 425 can be converted to a grayscalepatch (G). Subsequently, the background pixels 452 in the grayscalepatch G can be set to zero to create the background mask. Next, afunction f is applied to map G to generate the initial PSF (im_psf). Inone embodiment, the function f emphasizes pixels with a high intensity(e.g. im_sf=1000̂(G./255)). Subsequently, the background pixels 452 inthe initial PSF (im_psf) are set to be zero. Next, the initial PSF(im_psf) is normalized (sum (im_psf)=1). More specifically, the PSFfunction can be normalized to have the sum of all of the pixels equal toone (essentially, compute the sum of all pixels first, and then divideeach pixel by this sum, so the sum of all updated pixel values will beone). In summary, in one embodiment, the binary mask of the image patch425 is converted to gray scale intensity, with more weight assigned tohigher intensity values. Next, the PSF function is applied, and theregion is normalized.

With the present invention, in certain embodiments, the light trailpatch 425 is evaluated to estimate an initial PSF kernel. Asnon-exclusive examples, a refined PSF kernel can be generated bysupplying the initial PSF kernel to another PSF estimation algorithm(e.g., a matlab blind deconvolution, the PSF estimation algorithmdescribed in (i) U.S. patent application Ser. No. 12/740,664 filed onApr. 29, 2010, (ii) U.S. Provisional Application No. 61/617,358 filed onMar. 29, 2012, (iii) U.S. Provisional Application No. 61/617,539 filedon Mar. 29, 2012, or (iv) U.S. Provisional Application No. 61/640,492filed on Apr. 30, 2012, or another type of PSF estimation algorithm). Asfar as permitted, the contents of U.S. patent application Ser. No.12/740,664, U.S. Provisional Application No. 61/617,358, U.S.Provisional Application No. 61/617,539, and U.S. Provisional ApplicationNo. 61/640,492 are incorporated herein by reference.

Referring back to FIG. 3, next, at step 308, the control system utilizesthe generated point spread function to deblur a portion or the entireblurry image to generate the restored, adjusted image. Stated in anotherfashion, a sharp version of the blurred image can be reconstructed basedon either the initial PSF kernel (simple and fast computation) or arefined PSF kernel (more computation to reach higher quality).

In summary, in certain embodiments, with the present invention, the useronly needs to identify an image patch 425 containing the light trail424. After that, the control system 28 restores the blurred image 16.With this design, the user does not have to identify the motion blursize and/or the motion blur direction. Further, with this design, themotion kernel is not constrained to be linear motion (e.g. the methodcan be used for non-linear motion blur). As a result thereof, thepresent invention provides a simple and fast semi-automatic method toaddress a very challenging motion estimation problem by utilizing useridentified blurry light trail patch 425 with a simple PSF estimationmethod.

Stated in another fashion, the present invention is a semi-automaticmethod that allows user to indicate an image patch 425 that issubsequently analyzed by the control system 28 to determine the pointspread function of the image patch 425 or a portion thereof. Next, thecalculated point spread function can be applied to a portion of theimage to restore that portion of the image or the calculated pointspread function can be applied to the entire image to deblur the entireimage.

While the current invention is disclosed in detail herein, it is to beunderstood that it is merely illustrative of the presently preferredembodiments of the invention and that no limitations are intended to thedetails of construction or design herein shown other than as describedin the appended claims.

What is claimed is:
 1. A method for processing a blurred image, themethod comprising the steps of: displaying the blurred image on an imagedisplay; manually identifying an image patch in the displayed blurredimage; and calculating a point spread function for at least a portion ofthe image patch with a control system.
 2. The method of claim 1 whereinthe step of manually identifying an image patch includes the image patchhaving a captured light trail.
 3. The method of claim 2 wherein the stepof calculating a point spread function includes calculating the pointspread function for the captured light trail.
 4. The method of claim 2wherein the step of calculating a point spread function includes thestep of identifying light trail pixels in the image patch that havecaptured part of the captured light trail.
 5. The method of claim 4wherein the step of calculating a point spread function includescalculating the point spread function for the identified light trailpixels.
 6. The method of claim 1 further comprising the step ofdeblurring at least a portion of the blurred image with the controlsystem utilizing the calculated point spread function.
 7. The method ofclaim 1 further comprising the step of deblurring the blurred image withthe control system utilizing the calculated point spread function. 8.The method of claim 1 wherein the step of calculating a point spreadfunction includes utilizing a deconvolution method.
 9. A computerincluding an image display that displays the image, an identifier thatcan be used to manually identify the image patch, and a control systemthat utilizes the method of claim 1 to process the blurred image.
 10. Acamera including an image display that displays the image, an identifierthat can be used to manually identify the image patch, and a controlsystem that utilizes the method of claim 1 to process the blurred image.11. A method for processing a blurred image that includes a capturedlight trail, the method comprising the steps of: displaying the blurredimage on an image display; manually identifying an image patch thatincludes the captured light trail in the displayed blurred image;calculating a point spread function for the captured light trail with acontrol system; and deblurring at least a portion of the blurred imagewith the control system utilizing the calculated point spread function.12. The method of claim 11 wherein the step of calculating a pointspread function includes the steps of (i) identifying light trail pixelsin the image patch that have captured part of the captured light trail,and (ii) identifying background pixels in the image patch that have notcaptured part of the captured light trail.
 13. The method of claim 12wherein the step of calculating a point spread function includescalculating the point spread function for the identified light trailpixels.
 14. The method of claim 11 wherein the step of deblurringincludes deblurring the entire blurred image with the control systemutilizing the calculated point spread function.
 15. The method of claim11 wherein the step of calculating a point spread function includesutilizing a deconvolution method.
 16. A computer including an imagedisplay that displays the image, an identifier that can be used tomanually identify the image patch, and a control system that utilizesthe method of claim 11 to process the blurred image.
 17. A cameraincluding an image display that displays the image, an identifier thatcan be used to manually identify the image patch, and a control systemthat utilizes the method of claim 11 to process the blurred image.
 18. Acamera for capturing a captured image of a scene, the camera comprising:a capturing system for capturing the captured image of the scene; animage display for displaying the captured image of the scene; anidentifier for manually identifying an image patch including a lighttrail in the displayed captured image in the event the displayedcaptured image includes a light trail; and a control system thatcalculates a point spread function for at least a portion of the imagepatch.
 19. The camera of claim 18 wherein the control system (i)identifies light trail pixels in the image patch that have captured partof the captured light trail, and (ii) calculates the point spreadfunction for the identified light trail pixels.
 20. The camera of claim18 wherein the control system deblurs at least a portion of the blurredimage utilizing the calculated point spread function.